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Jiangchao Yao is currently an assistant professor at Cooperative Medianet Innovation Center, Shanghai Jiao Tong University and a research scientist in Shanghai AI Laboratory. His research mainly focuses on trustworthy machine learning and reasoning with the applications to medical AI. Before taking the faculty job, he was an algorithm expert in Data Analytics and Intelligence Lab, DAMO Academy, Alibaba Group. He received the dual PhD degree in Shanghai Jiao Tong University and University of Technology Sydney in 2019, under the supervision of Ya Zhang and Ivor W. Tsang. He has published more than 40 journal articles and conference papers, and contributed to one chapter to the book “Graph Neural Networks: Foundations, Frontiers, and Applications” (Springer). He has been the PC members of conferences including ICML, NeurIPS, ICLR, AAAI, IJCAI, and serve as the reviewers of TPAMI, TIP, TMLR, TNNLS, MLJ and TKDE.

Personal Website: https://sunarker.github.io/index.html

Interests
  • Label-Noise and Adversarially Robust Machine Learning
  • Class-, Subpopulation- and Domain- Imbalance Learning
  • Multimodal Robust Representation Learning
  • Universal Pretraining Methods for Medical Imaging Diagnosis.
Education

  • Ph.D., supervised by Ya Zhang, 2019

    Shanghai Jiao Tong University

  • Ph.D., supervised by Ivor W. Tsang, 2019

    University of Technology Sydney

  • B.Eng., 2013

    South China University of Technology